Monitoring of polishing pad texture in chemical mechanical polishing

ABSTRACT

An apparatus for chemical mechanical polishing includes a platen having a surface to support a polishing pad, a carrier head to hold a substrate against a polishing surface of the polishing pad, a pad conditioner to press an abrasive body against the polishing surface, an in-situ polishing pad monitoring system including an imager disposed above the platen to capture an image of the polishing pad, and a controller configured to receive the image from the monitoring system and generate a measure of polishing pad surface roughness based on the image. The controller can use machine-learning based image processing to generate the measure of surface roughness.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is divisional of U.S. application Ser. No. 16/820,299,filed Mar. 16, 2020, which claims priority to U.S. ProvisionalApplication Ser. No. 62/861,907, filed on Jun. 14, 2019, and to U.S.Provisional Application Ser. No. 62/821,935, filed on Mar. 21, 2019, thedisclosures of which are incorporated by reference.

TECHNICAL FIELD

The present disclosure relates to optical monitoring of a polishing padused in chemical mechanical polishing.

BACKGROUND

An integrated circuit is typically formed on a substrate by thesequential deposition of conductive, semiconductive, or insulativelayers on a silicon wafer. A variety of fabrication processes requireplanarization of a layer on the substrate. For example, one fabricationstep involves depositing a conductive filler layer on a patternedinsulative layer to fill the trenches or holes in the insulative layer.The filler layer is then polished until the raised pattern of theinsulative layer is exposed. After planarization, the portions of theconductive filler layer remaining between the raised pattern of theinsulative layer form vias, plugs and lines that provide conductivepaths between thin film circuits on the substrate.

Chemical mechanical polishing (CMP) is one accepted method ofplanarization. This planarization method typically requires that thesubstrate be mounted on a carrier head. The exposed surface of thesubstrate is placed against a rotating polishing pad. The carrier headprovides a controllable load on the substrate to push it against thepolishing pad. A polishing liquid, such as slurry with abrasiveparticles, is supplied to the surface of the polishing pad.

After the CMP process is performed for a certain period of time, thesurface of the polishing pad can become glazed due to accumulation ofslurry by-products and/or material removed from the substrate and/or thepolishing pad. Glazing can reduce the polishing rate or increasenon-uniformity on the substrate.

Typically, the polishing pad is maintained in with a desired surfaceroughness (and glazing is avoided) by a process of conditioning with apad conditioner. The pad conditioner is used to remove the unwantedaccumulations on the polishing pad and regenerate the surface of thepolishing pad to a desirable asperity. Typical pad conditioners includean abrasive head generally embedded with diamond abrasives which can bescraped against the polishing pad surface to retexture the pad.

SUMMARY

In one aspect, an apparatus for chemical mechanical polishing includes aplaten having a surface to support a polishing pad, a carrier head tohold a substrate against a polishing surface of the polishing pad, a padconditioner to press an abrasive body against the polishing surface, anin-situ polishing pad monitoring system including an imager disposedabove the platen to capture an image of the polishing pad, and acontroller configured to receive the image from the monitoring systemand generate a measure of polishing pad surface roughness based on theimage.

Implementations may include one or more of the following features.

The controller may be configured to operate as a machine learning basedimage processing system, and to input the image to the image processingsystem. The machine learning based image processing system may include asupervised learning module. The machine learning based image processingsystem may include a dimensional reduction module to receive the imageand output component values, and the controller may be configured toinput the component values to the image to the supervised learningmodule. The controller may be configured to input the image directly tothe supervised learning module. The controller may be configured tooperate the supervised learning module as an artificial neural network.

The controller may be configured to receive other data including a valuefor a parameter and may be configured to generate the measure ofpolishing pad surface roughness based on the image and the value of theparameter. The parameter may be a polishing control parameter, a stateparameter, a measurement from a sensor in the polishing system or ameasurement of the polishing pad by a sensor outside the polishingsystem. The parameter may be a platen rotation rate, a slurry dispenserate, a slurry composition, a number of substrates since the polishingpad was changed, or a measurement of the surface roughness of thepolishing pad by a stand-alone metrology station before the polishingpad was installed on the platen.

The controller may be configured to at least one of halt a conditioningprocess or adjust a conditioning parameter based on the measure ofpolishing pad surface roughness.

The imager may be radially movable over the platen. The imager may bemounted on an arm that can swing laterally over the platen.

In another aspect, a method of polishing includes bringing a substrateinto contact with a polishing pad on a platen, generating relativemotion between the substrate and the polishing pad, capturing an imageof the polishing pad with an optical sensor, and generating ameasurement of surface roughness of the polishing pad by inputting theimage to a machine learning based image processing system.

Implementations may include one or more of the following features.

Training data including a plurality of pairs of training images andtraining values may be received. A supervised learning algorithm in thelearning based image processing system may be trained using the trainingdata. A conditioning process may be halted or a conditioning parametermay be adjusted based on the measure of polishing pad surface roughness.

Certain implementations man include, but are not limited to, one or moreof the following advantages. The roughness of the polishing pad can bedetermined using a non-contact technique, so contamination and damage tothe polishing pad can be avoided. The polishing pad roughness can bedetermined accurately and quickly, and the conditioning process can beadjusted appropriately. Wafer-to-wafer non-uniformity (WTWNU) can bereduced. The roughness can be determined using non-contact technique, socontamination of the polishing pad can be avoided.

The details of one or more implementations are set forth in theaccompanying drawings and the description below. Other aspects, featuresand advantages will be apparent from the description and drawings, andfrom the claims.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a schematic side view, partially cross-sectional, of achemical mechanical polishing system that includes an optical monitoringsystem configured to detect a surface roughness of a polishing pad.

FIG. 2 is a schematic top view of a chemical mechanical polishingsystem.

FIG. 3 is a block diagram of a machine learning-based image processingsystem.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION

The chemical mechanical polishing process tends to reduce the surfaceroughness of the polishing pad, e.g., due to the glazing effect notedabove. Conditioning can be used to restore the surface roughness.However, the degree to which the pad is glazed, as well as the degree towhich conditioning restores surface roughness, can be non-uniform acrossthe polishing pad. As a result, even after conditioning there can benon-uniformity in the polishing pad surface roughness. Moreover,conditioning techniques can wear the pad at different rates across thepad, resulting in non-uniformities in the pad thickness, and can leaveperiodic scratching or scoring on the polishing pad surface. Contacttechniques, e.g., a profilometer, could be used to measure the surfaceroughness, but this may introduce a risk of contamination and may simplybe impractical to fit on the pad given the equipment (conditioner,carrier, etc.) that are already needed. However, the polishing pad canbe imaged, and the image can be fed to a trained machine learning modelthat outputs a surface texture measurement. A controller can then usethis measurement to adjust the conditioning process to achieve a targetsurface texture or improve uniformity of surface texture across thepolishing pad.

The term “surface texture” is used herein to encompass surfaceroughness, e.g., Ra, Rms, RSk, or Rp, and other irregularities in thepolishing pad surface, e.g., waviness, that are smaller than the normalgrooving or perforations pattern on the polishing pad. For example,assuming 20 mil deep grooves, surface texture might includeirregularities up to about 40-50 microns.

FIGS. 1 and 2 illustrate an example of a polishing system 20 of achemical mechanical polishing apparatus. The polishing system 20includes a rotatable disk-shaped platen 24 on which a polishing pad 30is situated. The platen 24 is operable to rotate about an axis 25. Forexample, a motor 22 can turn a drive shaft 28 to rotate the platen 24.The polishing pad 30 can be a two-layer polishing pad with an outerlayer 34 and a softer backing layer 32. The upper surface of thepolishing pad 30 provides a polishing surface 36.

The polishing system 20 can include a supply port or a combinedsupply-rinse arm 39 to dispense a polishing liquid 38, such as slurry,onto the polishing pad 30.

The polishing system 20 can also include a polishing pad conditioner 60to abrade the polishing pad 30 to maintain the polishing surface 36 in aconsistent abrasive state. The polishing pad conditioner 60 includes abase, an arm 62 that can sweep laterally over the polishing pad 30, anda conditioner head 64 connected to the base by the arm 64. Theconditioner head 64 brings an abrasive surface, e.g., a lower surface ofa disk 66 held by the conditioner head 64, into contact with thepolishing pad 30 to condition it. The abrasive surface can be rotatable,and the pressure of the abrasive surface against the polishing pad canbe controllable.

In some implementations, the arm 62 is pivotally attached to the baseand sweeps back and forth to move the conditioner head 64 in anoscillatory sweeping motion across polishing pad 30. The motion of theconditioner head 64 can be synchronized with the motion of carrier head70 to prevent collision.

Vertical motion of the conditioner head 64 and control of the pressureof conditioning surface on the polishing pad 30 can be provided by avertical actuator 68 above or in the conditioner head 64, e.g., apressurizable chamber positioned to apply downward pressure to theconditioner head 64. Alternatively, the vertical motion and pressurecontrol can be provided by a vertical actuator in the base that liftsthe entire arm 62 and conditioner head 64, or by a pivot connectionbetween the arm 62 and the base that permits a controllable angle ofinclination of the arm 62 and thus height of the conditioner head 64above the polishing pad 30.

The carrier head 70 is operable to hold a substrate 10 against thepolishing pad 30. The carrier head 70 is suspended from a supportstructure 72, e.g., a carousel or a track, and is connected by a driveshaft 74 to a carrier head rotation motor 76 so that the carrier headcan rotate about an axis 71. Optionally, the carrier head 70 canoscillate laterally, e.g., on sliders on the carousel or track 72; or byrotational oscillation of the carousel itself. In operation, the platenis rotated about its central axis 25, and the carrier head is rotatedabout its central axis 71 and translated laterally across the topsurface of the polishing pad 30. The carrier head 70 can include aflexible membrane 80 having a substrate mounting surface to contact theback side of the substrate 10, and a plurality of pressurizable chambers82 to apply different pressures to different zones, e.g., differentradial zones, on the substrate 10. The carrier head can also include aretaining ring 84 to hold the substrate.

The polishing system 20 includes an in-situ optical pad monitoringsystem 40 that generates a signal that represents the surface texture,e.g., the surface roughness, of the polishing pad 30. The in-situoptical pad monitoring system 40 includes an imager 42, e.g., a camera,positioned above the polishing pad, e.g., on a support arm 44. Forexample, the imager 42 can be a line scan camera and the pad monitoringsystem 40 could be configured to generate a 2-D image from multiplemeasurements by the line scan camera as the polishing pad 30 sweepsbelow the camera 40 due to rotation of the platen 24. Alternatively, theimager 42 can be a 2-D camera. The imager 42 can have a field of view 43of a portion of the surface 36 of the polishing pad 30. The camera caninclude a CCD array and optical components, e.g., lenses, to focus theimaging plane on the surface 36 of the polishing pad 30.

In some implementations, the imager 42 is positioned in a fixed radialposition and images a fixed radial zone of the polishing pad 30. In thissituation, the in-situ pad monitoring system 40 can generatemeasurements for the surface texture, e.g., surface roughness, at fixedradial position on the polishing pad 30.

However, in some implementations, the imager 42 is laterally movable,e.g., along a radius of the polishing pad 30. For example, referring toFIG. 2 , a base 46 that holds the support arm 42 could be configured topivot, thereby swinging the arm 42 (see arrow A) across the polishingpad 30 and carrying the imager 42 in an arc-shaped path. As anotherexample, the support arm 44 can be or include a linear rail, and theimager 42 can be movable by a linear actuator 46, e.g., a stepper motorwith linear screw, along the rail. By taking images of the polishing pad30 for different radial zones, the in-situ pad monitoring system 40 cangenerate measurements for the surface texture, e.g., surface roughness,at different radial positions on the polishing pad 30.

A controller 90, e.g., a general purpose programmable digital computer,receives the image from the in-situ polishing pad monitoring system 40,and can be configured to generate a measure of the surface texture,e.g., surface roughness, of the polishing pad 30 from the image. In thiscontext, the controller 90 (or the portion of the software that providesthe surface texture measurement) can be considered part of the padmonitoring system 40. As noted above, due to the polishing andconditioning processes, the surface roughness of the polishing pad 30changes over time, e.g., over the course of polishing multiple ofsubstrates.

The controller 90 can also be configured to control the pad conditioner60 system based on the value of the surface texture, e.g., surfaceroughness, received from the in-situ pad monitoring system 40. Forexample, when the measure of surface texture of the polishing pad 30meets a threshold, the controller 90 can halt the conditioning process.As another example, if the surface texture of the polishing pad meetsanother threshold, the controller 90 can generate an alert to theoperator of the polishing system 20, e.g., that the polishing orconditioning operation is not proceeding as expected.

As another example, if the in-situ pad monitoring system 40 generatesmeasurements for the surface texture, e.g., surface roughness, atdifferent radial positions on the polishing pad 30 (relative to the axisof rotation 25), then the controller 90 can use the measurements tocontrol the pad conditioner 60 to improve the uniformity of the surfacetexture, e.g., surface roughness. For example, the controller 90 cancontrol the sweep of the conditioner arm 62 to control the dwell time ofthe conditioner disk 64 in the different radial zones on the polishingpad. For example, if the surface roughness in a radial zone needs to beincreased, the dwell time can be increased, whereas if the surfaceroughness in a radial zone needs to be decreased, the dwell time can bedecreased.

Referring to FIG. 3 , the image from the in-situ pad monitoring system40 is fed into a trained machine-vision image processing system 100. Themachine-vision image processing system 100 is configured to output avalue representative of the texture, e.g., the surface roughness, of theportion of the polishing surface 36 within the field of view 43 of theimager 42. The machine-vision image processing system 100 can beimplemented as part of the controller 90. The machine-vision imageprocessing system 100 can incorporate various machine learningtechniques. For example, the machine-vision image processing system 100can include a neural network, but other approaches are possible, e.g., anaïve Bayes classifier or support vector machine.

FIG. 3 illustrates functional blocks that can be implemented for themachine-learning based image processing system 100. These functionalblocks can include an optional dimensional reduction module 110 to carryout dimensional reduction of the image, and a supervised learning module120 (shown implemented as a neutral network). The supervised learningmodule 120 implements a supervised learning algorithm to generate afunction to output a measurement of the surface texture, e.g., surfaceroughness, based on the image (or the dimensionally reduced data fromthe image). These functional blocks can be distributed across multiplecomputers.

The output of the supervised learning module 120 can be fed to a processcontrol system 130, which can be implemented as part of the controller90, to adjust the polishing process based on the surface texturemeasurement. For example, the process control system 130 can detect aconditioning endpoint and halt conditioning and/or adjust theconditioning parameters (e.g., sweep profile, conditioner head pressure,etc.) during or between polishing processes to reduce non-uniformity ofthe surface texture, e.g., surface roughness, of the polishing surface36, based on the measurement of the surface texture, e.g., surfaceroughness.

Assuming the machine learning module 120 is a neutral network, theneural network includes a plurality of input nodes 122 for eachprincipal component, a plurality of hidden nodes 124 (also called“intermediate nodes” below), and an output node 126 that will generatethe surface texture, e.g., surface roughness, measurement. In general, ahidden node 124 outputs a value that a non-linear function of a weightedsum of the values from the input nodes 122 to which the hidden node isconnected.

For example, the output of a hidden node 124, designated node k, can beexpressed as:

tanh(0.5*α_(k1)(I ₁)+α_(k2)(I ₂)+ . . . +α_(kM)(I _(M))+b_(k))  Equation 1

where tanh is the hyperbolic tangent, a_(kx) is a weight for theconnection between the k^(th) intermediate node and the x^(th) inputnode (out of M input nodes), and I_(M) is the value at the M^(th) inputnode. However, other non-linear functions can be used instead of tanh,such as a rectified linear unit (ReLU) function and its variants.

The optional dimensional reduction module 110 will reduce the image to amore limited number of component values 112, e.g., L component values.The neural network 120 includes an input node 122 for each componentinto which the image is reduced, e.g., where the dimensional reductionmodule 110 generates L component values the neural network 120 willinclude at least input nodes N₁, N₂ . . . N_(L).

However, supervised learning module 120 may optionally receive one ormore inputs 114 other than the image or component values. The otherinput(s) 114 can include a measurement from another sensor in thepolishing system, e.g., a measurement of temperature of the pad by atemperature sensor, or a measurement of slurry flow rate from a flowsensor. The other input(s) can include a value of a polishing controlparameter, e.g., a platen rotation rate, slurry flow rate, or slurrycomposition. The polishing control parameter value can be obtained froma polishing recipe stored by the controller 90. The other input(s) caninclude a state parameter tracked by the controller, e.g., anidentification of the variety of pad (such as manufacturer, brand name,pad composition, grooving pattern, etc.) being used, or a number ofsubstrates polished since the pad was changed). The other input(s) caninclude a measurement from a sensor that is not part of the polishingsystem, e.g., a measurement of the surface texture, e.g., surfaceroughness, of the polishing pad by a stand-alone metrology stationbefore the pad is installed on the platen. This permits the supervisedlearning module 120 to take into account these other processing orenvironmental variables in calculation of the surface texture, e.g.surface roughness. Assuming the supervised learning module 120 is aneural network, the neural network can include one or more other inputnodes (e.g., node 122 a) to receive the other data.

The architecture of the neural network 120 can vary in depth and width.For example, although the neural network 120 is shown with a singlecolumn of hidden nodes 124, it could include multiple columns. Thenumber of intermediate nodes 124 can be equal to or greater than thenumber of input nodes 122. The neural network can be fully connected ora convolutional network.

Before being used for, e.g., processing of device wafers, the supervisedlearning module 120 needs to be configured.

As part of a configuration procedure, the supervised learning module 120receives training data, which can include a plurality of training imagesand a plurality of training values, e.g., surface texture values, e.g.,surface roughness values. Each reference image has an training value,i.e., the training data includes pairs of images and training values.

For example, images can be take of various pad samples. In addition,measurements of the surface roughness of the samples can be performedwith metrology equipment, e.g., a contact profilometer, interferometeror confocal microscope. Each training image can thus be associated witha training value which is the surface roughness of the sample from whichthe image was taken.

In some implementations, a data store can store multiple of sets oftraining data. The different sets of training data could correspond todifferent types of polishing pads, e.g., different compositions and/orgroove patterns. The supervised learning module 120 can receive aselection of a set of training data from the operator of thesemiconductor fabrication plant, e.g., through a user interface.

Training of the supervised learning module 120 can be performed usingconventional techniques. For example, for a neural network, training canbe performed by backpropagation using the training images and thetraining values. For example, while the neural network is operating in atraining mode, the reduced dimensionality values of the training imageare fed to the respective input nodes N₁, N₂ . . . N_(L) while thetraining value V is fed to the output node 126. This can be repeated foreach pair of an image and a training value. Where the supervisedlearning module 120 receives inputs other than the image or componentvalues, values for these parameters may also be fed to the machinelearning module as training data.

Once the training has been performed, the trained instantiation of thesupervised learning module can then be used, e.g., as described above.That is, during processing of a substrate, an image of the polishing padas well as the other parameter values can be fed to the trainedsupervised learning module 120, which outputs the value for the surfacetexture, e.g., surface roughness. The surface texture value, e.g.,surface roughness, can then be used to control the conditioningoperation, e.g., as discussed above.

The in-situ polishing pad monitoring system can be used in a variety ofpolishing systems. Either the polishing pad, or the carrier head, orboth can move to provide relative motion between the polishing surfaceand the substrate. The polishing pad can be a circular (or some othershape) pad secured to the platen, a tape extending between supply andtake-up rollers, or a continuous belt. The polishing pad can be affixedon a platen, incrementally advanced over a platen between polishingoperations, or driven continuously over the platen during polishing. Thepad can be secured to the platen during polishing, or there can be afluid bearing between the platen and polishing pad during polishing. Thepolishing pad can be a standard (e.g., polyurethane with or withoutfillers) rough pad, a soft pad, or a fixed-abrasive pad.

In addition, although the foregoing description focuses on monitoringduring polishing, the measurements of the polishing pad could beobtained before or after a substrate is being polished, e.g., while asubstrate is being transferred to the polishing system.

The controller and its functional operations can be implemented indigital electronic circuitry, or in computer software, firmware, orhardware, or in combinations of them. A controller that is “configured”to perform operations has sufficient software, firmware or hardware toactually perform the operations, and is not merely capable of beingprogrammed or modified to perform the operations.

Embodiments can be implemented as one or more computer program products,i.e., one or more computer programs tangibly embodied in an informationcarrier, e.g., in a non-transitory machine-readable storage medium or ina propagated signal, for execution by, or to control the operation of,data processing apparatus, e.g., a programmable processor, a computer,or multiple processors or computers. A computer program (also known as aprogram, software, software application, or code) can be written in anyform of programming language, including compiled or interpretedlanguages, and it can be deployed in any form, including as astand-alone program or as a module, component, subroutine, or other unitsuitable for use in a computing environment. A computer program does notnecessarily correspond to a file. A program can be stored in a portionof a file that holds other programs or data, in a single file dedicatedto the program in question, or in multiple coordinated files (e.g.,files that store one or more modules, sub-programs, or portions ofcode). A computer program can be deployed to be executed on one computeror on multiple computers at one site or distributed across multiplesites and interconnected by a communication network.

The processes and logic flows described in this specification can beperformed by one or more programmable processors executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application-specific integrated circuit).

A number of embodiments of the invention have been described.Nevertheless, it will be understood that various modifications may bemade without departing from the spirit and scope of the invention.Accordingly, other embodiments are within the scope of the followingclaims.

What is claimed is:
 1. An apparatus for chemical mechanical polishing,comprising: a platen having a surface to support a polishing pad; acarrier head to hold a substrate against a polishing surface of thepolishing pad; a pad conditioner to press an abrasive body against thepolishing surface; an in-situ polishing pad monitoring system includingan imager disposed above the platen to capture an image of the polishingpad; and a controller configured to receive the image from themonitoring system, to receive other data including a value for aparameter, and to generate a measure of polishing pad surface texturebased on the image and the value of the parameter.
 2. The apparatus ofclaim 1, wherein the parameter comprises a polishing control parameter,a state parameter, a measurement from a sensor in the polishing systemor a measurement of the polishing pad by a sensor outside the polishingsystem.
 3. The apparatus of claim 2, herein the parameter comprises aplaten rotation rate, a slurry dispense rate, a slurry composition, anumber of substrates since the polishing pad was changed, or ameasurement of the surface roughness of the polishing pad by astand-alone metrology station before the polishing pad was installed onthe platen.
 4. The apparatus of claim 1, wherein the controller isconfigured to at least one of halt a conditioning process or adjust aconditioning parameter based on the measure of polishing pad surfacetexture.
 5. The apparatus of claim 1, wherein the controller isconfigured to operate as a machine learning based image processingsystem, and to input the image to the image processing system.
 6. Theapparatus of claim 5, wherein the machine learning based imageprocessing system comprises a supervised learning module.
 7. Theapparatus of claim 6, wherein the machine learning based imageprocessing system comprises a dimensional reduction module to receivethe image and output component values, and wherein the controller isconfigured to input the component values to the image to the supervisedlearning module.
 8. The apparatus of claim 6, wherein the controller isconfigured to input the image directly to the supervised learningmodule.
 9. The apparatus of claim 6, wherein the controller isconfigured to operate the supervised learning module as an artificialneural network.
 10. An apparatus for chemical mechanical polishing,comprising: a platen having a surface to support a polishing pad; acarrier head to hold a substrate against a polishing surface of thepolishing pad; a pad conditioner to press an abrasive body against thepolishing surface; an in-situ polishing pad monitoring system includingan imager disposed above the platen to capture an image of the polishingpad, wherein the imager is radially movable over the platen; and acontroller configured to receive the image from the monitoring system,to receive other data including a value for a parameter,
 11. Theapparatus of claim 10, wherein the imager is mounted on a pivotable armto swing laterally over the platen.
 12. A method of polishing,comprising: bringing a substrate into contact with a polishing pad on aplaten; generating relative motion between the substrate and thepolishing pad; capturing an image of the polishing pad with an opticalsensor; receiving other data including a value for a parameter; andgenerating a measurement of surface texture of the polishing pad byinputting the image and the value of the parameter to a machine learningbased image processing system.
 13. The method of claim 12, wherein theparameter comprises a polishing control parameter, a state parameter, ameasurement from a sensor in the polishing system or a measurement ofthe polishing pad by a sensor outside the polishing system.
 14. Themethod of claim 12, herein the parameter comprises a platen rotationrate, a slurry dispense rate, a slurry composition, a number ofsubstrates since the polishing pad was changed, or a measurement of thesurface roughness of the polishing pad by a stand-alone metrologystation before the polishing pad was installed on the platen.
 15. Themethod of claim 12, comprising receiving training data including aplurality of pairs of training images and training values, and traininga supervised learning algorithm in the learning based image processingsystem using the training data.
 16. The method of claim 15, wherein thetraining values comprise surface texture values.
 17. The method of claim16, wherein the training values comprise surface roughness values. 18.The method of claim 12, comprising halting a conditioning process oradjusting a conditioning parameter based on the measure of polishing padsurface texture.